All R scripts from the 2018 workshop can be downloaded from here. - The scripts make heavy use of the amt package. You can download our paper describing the package here. Note: code in the MultipleAnimals.R scripts takes a long time to run (close to 2 hours) and has been updated/upgraded significantly in 2019.
TestVignetteMovebank2018.R: reads in fisher data from movebank, illustrates some simple plotting methods, explores large-scale movement characteristics (net-squared displacement, MCP and KDE home ranges over short time frames) and fine-scale movement characteristics (step lengths, turn angles). This program also illustrates how the amt package can be used for initial data development when fitting Resource Selection Functions (RSFs) or Step-Selection Functions (SSFs). Writes out the combined use and availability data for model fitting.
mergedat.R: Merges together original Use/available data written out by TestVignetteMovebank2018.R with annotated Env-Data.
TestTimeCube.R: illustrates how one can plot a space-time cube showing how animals move in space and time, with color mapped to categories of an environmental variable (elevation in this case).
FisherRSF.R: application of RSF models to fisher data. Demonstrates the use of list columns which make it easy to fit models to individual animals.
FisherSSF.R: application of SSFs to fisher data.
MultipleAnimals.R: demonstration of some methods for modeling data from multiple individuals. Note: the code in this file takes a LONG time to run (close to 2 hours).
All R scripts from the 2019 workshop can be downloaded from here. For the EnvDATA visualizations, you will also need a large set of rasters that can be accessed here. You will also need a second set of rasters for annotating used and available locations prior to fitting RSFs and SSFs, which you can get here
TestVignetteMovebank.R: checks for and installs necessary R packages, reads in fisher data from movebank, illustrates some simple plotting methods, explores large-scale movement characteristics (net-squared displacement, MCP and KDE home ranges over short time frames) and fine-scale movement characteristics (step lengths, turn angles).
FisherRSF.R: application of RSF models to fisher data. Demonstrates the use of list columns which make it easy to fit models to individual animals.
FisherSSF.R: application of SSFs to fisher data.
MultipleAnimals.R: demonstration and comparison of various methods for modelling data from multiple individuals (two-step approach versus mixed models).
EnvDATAvizTrack.R: provides some examples of how to visualize the results of track annotations from EnvDATA using base R some popular packages.
EnvDATAvizRaster.R: demonstrates how to visualize the results of gridded (raster) annotations from EnvDATA (www.movebank.org/node/6607). This builds on EnvDATAintroVis.R with examples for how to work with rasters and projections using the raster and rasterVis packages.
EnvDATAvizAnimate.R: demonstrates how to build animated tracking maps using tracking data in Movebank, environmental covariates in track and raster annotations from EnvDATA, and the moveVis package written by Jakob Schwalb-Willmann. Note, these examples can take quite a bit of time to run!
simulations.R: demonstrates methods for simulating space use from fitted RSF and SSFs.